Kernel Fuzzy Clustering Research Articles

Selective multiple kernel fuzzy clustering with locality preserved ensemble

Multiple kernel fuzzy clustering (MKFC) has demonstrated promising performance in capturing the non-linear relationships within data. However, its effectiveness relies heavily on the appropriate selection of the fuzzification coefficient and kernel functions. To address this challenge, this paper proposes a novel clustering ensemble approach for improving the robustness and accuracy of the MKFC algorithm. The proposed method employs a multi-objective evolutionary optimization approach to heuristically select the optimal fuzzification and kernel coefficients. By employing the selected coefficients, the MKFC algorithm generates a diverse set of accurate candidate clustering results. Subsequently, a locality preserved ensemble mechanism is introduced to derive the final partition matrix, ensuring that all candidate clusterings within the Pareto non-dominated set contribute to the final consensus matrix. Moreover, this mechanism incorporates the knowledge about the locality of the dataset via a graph regularization term, thereby further enhancing the clustering performance. Comprehensive experiments conducted on widely adopted benchmark datasets demonstrate the superiority of the proposed method over the state-of-the-art approaches.

Reconstruction-Aware Kernelized Fuzzy Clustering Framework Incorporating Local Information for Image Segmentation

Kernelized fuzzy C-means clustering with weighted local information is an extensively applied robust segmentation algorithm for noisy image. However, it is difficult to effectively solve the problem of segmenting image polluted by strong noise. To address this issue, a reconstruction-aware kernel fuzzy C-mean clustering with rich local information is proposed in this paper. Firstly, the optimization modeling of guided bilateral filtering is given for noisy image; Secondly, this filtering model is embedded into kernelized fuzzy C-means clustering with local information, and a novel reconstruction-filtering information driven fuzzy clustering model for noise-corrupted image segmentation is presented; Finally, a tri-level alternative and iterative algorithm is derived from optimizing model using optimization theory and its convergence is strictly analyzed. Many Experimental results on noisy synthetic images and actual images indicate that compared with the latest advanced fuzzy clustering-related algorithms, the algorithm presented in this paper has better segmentation performance and stronger robustness to noise, and its PSNR and ACC values increase by about 0.16–3.28 and 0.01–0.08 respectively.

Personalized Recommendation Method of E-Commerce Products Based on In-Depth User Interest Portraits

In dynamic e-commerce environments, researchers strive to understand users' interests and behaviors to enhance personalized product recommendations. Traditional collaborative filtering (CF) algorithms have encountered computational challenges such as similarity errors and user rating habits. This research addresses these issues by emphasizing user profiling techniques. This article proposes an innovative user profile updating technique that explores the key components of user profile (basic information, behavior, and domain knowledge). An enhanced kernel fuzzy mean clustering algorithm constructs a dynamic user portrait based on domain knowledge mapping. This dynamic portrait is combined with e-commerce personalized recommendation to improve the accuracy of inferring user interests, thus facilitating accurate recommendation on the platform. The method proposed in this article greatly improves the overall performance and provides strong support for developing smarter and more personalized e-commerce product recommendation systems.

Knowledge-induced Multiple Kernel Fuzzy Clustering.

The introduction of domain knowledge opens new horizons to fuzzy clustering. Then knowledge-driven and data-driven fuzzy clustering methods come into being. To address the challenges of inadequate extraction mechanism and imperfect fusion mode in such class of methods, we propose the Knowledge-induced Multiple Kernel Fuzzy Clustering (KMKFC) algorithm. Firstly, to extract knowledge points better, the Relative Density-based Knowledge Extraction (RDKE) method is proposed to extract high-density knowledge points close to cluster centers of real data structure, and provide initialized cluster centers. Moreover, the multiple kernel mechanism is introduced to improve the adaptability of clustering algorithm and map data to high-dimensional space, so as to better discover the differences between the data and obtain superior clustering results. Secondly, knowledge points generated by RDKE are integrated into KMKFC through a knowledge-influence matrix to guide the iterative process of KMKFC. Thirdly, we also provide a strategy of automatically obtaining knowledge points, and thus propose the RDKE with Automatic knowledge acquisition (RDKE-A) method and the corresponding KMKFC-A algorithm. Then we prove the convergence of KMKFC and KMKFC-A. Finally, experimental studies demonstrate that the KMKFC and KMKFC-A algorithms perform better than thirteen comparison algorithms with regard to four evaluation indexes and the convergence speed.

Random Feature-Based Collaborative Kernel Fuzzy Clustering for Distributed Peer-to-Peer Networks

Kernel clustering has the ability to get the inherent nonlinear structure of the data. But the high computational complexity and the unknown representation of the kernel space make it unavailable for the data clustering in distributed peer-to-peer (P2P) networks. To solve this issue, we propose a new series of random feature-based collaborative kernel clustering algorithms in this article. In the most basic algorithm, each node in a distributed P2P network first maps its data into a low-dimensional random feature space with the approximation of the given kernel by using the random Fourier feature mapping method. Then, each node independently searches the clusters with its local data and the collaborative knowledge from its neighbor nodes, and the distributed clustering is performed among all network nodes until reaching the global consensus result, i.e., all nodes have the same cluster centers. In addition, an improved version is designed with assignment of feature weights, which is optimized by the maximum-entropy technique to extract important features for the cluster identification. What’s more, to relief the impact of different kernel functions and related parameters on clustering results, the combination of multiple kernels rather than a single kernel is adopted for the low-dimensional approximation, and the optimized weights are assigned to provide the guidance on the choice of the kernels and their parameters and discover significant features at the same time. Experiments on synthetic and real-world datasets show that the proposed methods achieve similar and even better results than the traditional kernel clustering methods on various performance metrics, including the average classification rate, the average normalized mutual information, and the average adjusted rand index. More importantly, the low-dimensional random features approximated to kernels and the distributed clustering mechanism adopted in these methods bring the greatly lower temporal complexity.

A noise robust kernel fuzzy clustering based on picture fuzzy sets and KL divergence measure for MRI image segmentation

A noise robust kernel fuzzy clustering based on picture fuzzy sets and KL divergence measure for MRI image segmentation

Efficient kernel fuzzy clustering via random Fourier superpixel and graph prior for color image segmentation

The kernel fuzzy clustering algorithms can explore the non-linear relations of pixels in an image. However, most of kernel-based methods are computationally expensive for color image segmentation and neglect the inherent locality information in images. To alleviate these limitations, this paper proposes a novel kernel fuzzy clustering framework for fast color image segmentation. More specifically, we first design a new superpixel generation method that uses random Fourier maps to approximate Gaussian kernels and explicitly represent high-dimensional features of pixels. Clustering superpixels instead of large-sized pixels speeds up the segmentation of a color image significantly. More importantly, the features of superpixels used by fuzzy clustering are also calculated in the approximated kernel space and the local relationships between superpixels are depicted as a graph prior and appended into the objective function of fuzzy clustering as a Kullback–Leibler divergence term. This results in a new fuzzy clustering model that can further improve the accuracy of the image segmentation. Experiments on synthetic and real-world color image datasets verify the superiority and high efficiency of the proposed approach.

Guided filter-driven kernel fuzzy clustering with local information for noise image segmentation

Guided filter-driven kernel fuzzy clustering with local information for noise image segmentation

A novel solution of deep learning for endoscopic ultrasound image segmentation: enhanced computer aided diagnosis of gastrointestinal stromal tumor

Gastrointestinal stromal tumor is one of the critical tumors that doctors do not suggest to get frequent endoscopy, so there is a need for a diagnosis system which can process ultrasound images and figure out the tumor. Many gastrointestinal tumor diagnosis methods were developed, but all of these methods used manual contour rather than automatic segmentation. The research adopts enhanced automatic segmentation to improve the diagnosis of the gastrointestinal stromal tumor with deep convolutional neural networks. This solution’s proposed system is an enhanced automated segmentation methodology using multi-scale Gaussian kernel fuzzy clustering and multi-scale vector field convolution, which segments the ultrasound image automatically into the region of interest (the infected area). Convolutional Neural Network with Class Activation Mapping is done to diagnose an image with the tumor for Four datasets, namely (USS1, SH Hospital, SNUH, BUSI). This proposed system helps to get a clearer tumor image, and the accuracy has increased from 84.275% to 88.4%, and the processing time has reduced from 28.525% to 24.575%. The proposed solution enhanced Automatic Segmentation helped to get clearer tumor image which resulted in increased accuracy and decreased performance time compared to the state-of-the-art. Automatic segmentation overcomes the dependency on the expert for drawing the Region of Interest (ROI).

Research on Audit Opinion Prediction of Listed Companies Based on Sparse Principal Component Analysis and Kernel Fuzzy Clustering Algorithm

The prediction of audit opinions of listed companies plays a significant role in the security market risk prevention. By introducing machine learning methods, many innovations can be implemented to improve audit quality, lift audit efficiency, and cultivate the keen insight of auditors. However, in a realistic environment, category imbalance and critical feature selection exist in the prediction model of company audit opinions. This paper firstly combines batched sparse principal component analysis (BSPCA) with kernel fuzzy clustering algorithm (KFCM) and proposes a sparse-kernel fuzzy clustering undersampling method (S-KFCM) to deal with the imbalance of sample categories. This method adopts the kernel fuzzy clustering algorithm to down-sample the normal samples, and their features are extracted from abnormal sample sets based on the group sparse component method. The sparse normal sample set can maintain the original distribution space structure and highlight the classification boundary samples. Secondly, considering the company’s characteristic attributes and data sources, 448 original variables are grouped, and then BSPCA is used for feature screening. Finally, the support vector machine (SVM) is adopted to complete the classification prediction. According to the empirical results, the SKFCM-SVM model has the highest prediction accuracy.

Internet of things network intrusion detection model based on quantum artificial fish group and fuzzy kernel clustering algorithm

AbstractThe application of internet of things (IoT) devices in 5G/B5G has benefited many industries, such as automated production, smart agriculture and autopilot, and has provided access to billions of smart devices. However, the rapid growth of IoT devices has also brought network security issues. The purpose of this paper is to study the network intrusion detection model based on quantum artificial fish population and fuzzy kernel clustering algorithm. In this paper, the network intrusion detection model based on traditional fuzzy C‐means (FCM) clustering has poor classification effect and is prone to local extremum. A semi‐supervised fuzzy kernel–clustering algorithm based on quantum artificial fish group is proposed. The algorithm adopts a small amount of tag data and many unknown tag data to generate the classification of network intrusion detection and constructs a new objective function of FCM algorithm by means of kernel distance. In the simulation experiment of KDD Cup 99 network intrusion detection data, compared with the intrusion detection model based on FCM, particle swarm optimization‐FCM, the detection rate of the proposed algorithm in the known attack and unknown attack is 92.5% and 84.9%, respectively. The results show that the proposed intrusion detection algorithm has better detection rate.

The intrusion data mining method for distributed network based on fuzzy kernel clustering algorithm

In order to overcome the shortcomings of intrusion data mining method, which is easy to fall into local extremum and leads to poor mining effect, a distributed network intrusion data mining method based on fuzzy kernel clustering algorithm is studied. In this method, a new feature vector is established by using the Gauss kernel function which accords with Mercer condition through the fuzzy kernel clustering algorithm, so that the data pattern space can be effectively mapped to the high-dimensional feature space. When the new data does not meet the clustering condition, a new cluster set and sub box are established, until the end of all data clustering, the distributed network intrusion data mining is realised. The experimental results show that the accuracy of mining is higher than 95%, the false alarm rate and the false alarm rate are lower than 2%, and the total mining time is only 446 ms.

Double iterative learning-based polynomial based-RBFNNs driven by the aid of support vector-based kernel fuzzy clustering and least absolute shrinkage deviations

Recently, the polynomial-based radial basis function neural networks (P-RBFNNs) have been successfully applied to regression tasks. However, the redundant and non-linear partitioned data easily interfere with accurate partitioning of clusters completed in P-RBFNNs, affecting the regression performance of this existing model. Because the squared error is used as the cost function of the learning method, P-RBFNNs are sensitive to noise interference. In order to cope with these problems, this study introduces a double iterative learning-based polynomial based-RBFNNs (DP-RBFNNs) modeling that focuses on the formation of architectures to improve the accuracy of regression performance as well as enhance the robustness through double iterative learning as follows: a) support vector-based Gaussian kernel fuzzy c-means (SV-GKFCM) as a kind of the support vector-based kernel fuzzy clustering are designed to determine connections (weights) between the input and hidden layers of the proposed model. SV-GKFCM helps effectively reduce the number of redundant data to re-modify the partitioning of clusters in the DP-RBFNNs. In addition, the cluster centers can be accurately updated from the non-linear partitioned data with the aid of Gaussian kernel distance in SV-GKFCM; b) least absolute shrinkage deviations (LASD) as a robust estimation are designed to update connection weights between the hidden and output layers. The SAE (sum of absolute error) function in the LASD method is used as a cost function to reduce the noise interference in the procedure of weight estimation as well as enhance the robustness of the DP-RBFNNs. The superiority of the proposed model is demonstrated through the experimental results.

Spatial information-based intuitionistic fuzzy kernel clustering algorithm for cerebral hemorrhage image segmentation

Spatial information-based intuitionistic fuzzy kernel clustering algorithm for cerebral hemorrhage image segmentation

Cloud-based intrusion detection using kernel fuzzy clustering and optimal type-2 fuzzy neural network

Nowadays, digital data is an important asset for every organization. With the advent of cloud computing, cloud service providers (CSPs) offer the required infrastructure to end-users for storage and provide flexibility in accessing data. Since the users access the data from the cloud through the Internet, the data stored in the cloud are exposed to various intrusions. Intrusion detection is considered to be a significant issue in the cloud. The existing techniques are capable to detect well-known attacks but fall short in detecting low frequent attacks. To address this issue, we propose a novel intrusion detection system (IDS) in the cloud using a combination of kernel fuzzy c-means clustering (KFCM) and an optimal type-2 fuzzy neural network (OT2FNN). To achieve this, we optimally select the parameters of T2FNN using the lion optimization algorithm (LOA) for weight optimization. The proposed IDS detects the intrusion and allow only normal data to be stored in the cloud. Simulation results on the NSL-KDD dataset show that the proposed IDS system gives better results than the existing IDS systems in terms of precision, recall, and F-measure.

Target Positioning Algorithm Based on RSS Fingerprints of SVM of Fuzzy Kernel Clustering

The positioning technology based on receive signal strength (RSS) fingerprints has become one of the hottest research spots with its advantages of simple deployment, low cost, and single parameter. However, in the limited space, the multipath and shadowing, result in poor separability of the fingerprint data, and low accuracy of target localization. In this paper, a novel RSS fingerprints positioning algorithm that is based on fuzzy kernel clustering SVM is proposed to combat the multipath and shadowing effects. The first step of the proposed positioning algorithm is to use kernel function to map the traditional fingerprints sample data to high-dimensional feature space to generate fuzzy classes. The second step is to generate binary-class SVM of fuzzy class based on the relationship between classes and internal discrete information of each class. After that, we can use the binary fuzzy class SVM to dichotomize the classified fingerprints in the first step, and combine these dichotomous SVMs into a handstand classification binary tree. And thus, the proposed positioning algorithm achieves quick and accurate positioning. Experimental results show that the positioning accuracy and locating stability of proposed positioning algorithm are improved by 38.73% and 59.26%, respectively, compared with the traditional RSS fingerprints algorithm.

Recognition of ocean floor manganese nodules by deep kernel fuzzy C-means clustering of hyperspectral images

Recognition of ocean floor manganese nodules by deep kernel fuzzy C-means clustering of hyperspectral images

WITHDRAWN: A new proposal for the segmentation of breast lesion in mammogram images using optimized kernel fuzzy clustering algorithm

Abstract Breast Cancer is one of the fatal diseases which is caused due to the un natural development of the tissues in the breasts which are abnormal. The level of sarcoma or the stage of the cancer is mostly relied upon the doctor’s analysis. In order to provide a technical contribution which supports the doctor to take decision, this paper is intended to develop a framework which can help in determining the stage in which the cancer is at present. The major issues in the prediction of breast cancer through mammograms are the diverged artifacts, similar breast tissues and lower contrast on the boundary between skin and air. To overcome these issues, Optimized Kernel Fuzzy Clustering Algorithm is developed (OKFCA) and to determine the cancer portions in mammogram images. The OKFCA algorithm has described to identify the segmented regions in Mammogram Image Analysis Society (MIAS) database. The proposed segmentation algorithm is carried out with pre-processed mammogram images, noise free image that was obtained by using Hybrid Denoising Filter (HDF) algorithm and the proposed OKFCA is a significant approach to find out the cancer segment of mammogram image. Data clustering facilitates to place data of similar types in one group and of dissimilar types in different group. The results from the experiments which were carried on the MIAS data confirms the efficiency of the proposed system in terms of accuracy when compared to that of the famous K-Means, OKFCA and Otsu methods.

Empirical Analysis of Stock Investment Based on β-KFCM

Cluster analysis is an analysis method that classifies data sets. It measure the similarity between different data and classifies them on the basis of similarity, but the classification is unknown. In recent years, with the popularization and improvement of information technology, a large number of diversified data have been produced in various industries. In order to extract data features and classify them, this paper, based on the study of fuzzy C-means clustering algorithm, take the systematic risk coefficient as the weight, expand the model to get the weighted fuzzy kernel clustering algorithm: -KFCM fuzzy clustering algorithm and solve the algorithm. Based on the empirical research, we choose six Accounting Indicators to study 50 stocks in A-share market, including current ratio, cash ratio, year-on-year growth rate of operating revenue, return rate of total assets, net sales interest rate and asset liability ratio. According to the empirical research, we can get the results that the investment grade is divided into three categories, among them, Category I is the recommended high-quality investment variety, category II and category III are next to it.

Application of artificial fish swarm optimization semi-supervised kernel fuzzy clustering algorithm in network intrusion

For the unsupervised learning based clustering algorithm, the intrusion detection rate is low, and the training sample based on supervised learning clustering algorithm is insufficient. A semi-supervised kernel fuzzy C-means clustering algorithm based on artificial fish swarm optimization (AFSA-KFCM) is proposed. Firstly, the kernel function is used to change the distance function in the traditional semi-supervised fuzzy C-means clustering algorithm to define a new objective function, thus improving the probabilistic constraints of the fuzzy C-means algorithm. Then, the artificial fish swarm algorithm with strong global optimization ability is used to improve the KFCM sensitivity to the initial cluster center and easy to fall into the local extremum, thus improving the convergence speed and improving the classification effect. The test results in the Wine and IRIS public datasets show that the AFSA-KFCM clustering algorithm is superior to the traditional algorithm in clustering accuracy and time efficiency. At the same time, the experimental results in KDDCUP99 experimental data show that the algorithm can obtain the ideal detection rate and false detection rate in intrusion detection.